Drone world action model improves visual navigation accuracy and efficiency
DroneWAM: Efficient World Action Model for Drone Visual Navigation
Computer Vision and Pattern Recognition
Summary
Drones need to predict how their movements will change the view they see to navigate well. The authors created DroneWAM, a smart model that imagines future views in a simpler way to save time and computing power. They also made the model decide how far ahead to predict based on the scene, making it even faster and better. They tested this on a new drone flight dataset with different conditions, and DroneWAM navigated more accurately than other methods.
What this means in practice
- •For drone developers: Use DroneWAM to improve autonomous drone navigation accuracy while reducing computational demands during flight.
- •For robotics engineers: Implement adaptive rollout in prediction models to dynamically allocate computational resources based on environmental complexity in aerial robots.
Authors
Liang Yao, Fan Liu, Hongbo Lu, Wei Xu, Jianyu Jiang, Yijun Shen, Chuanyi Zhang, Pai Peng
Abstract
World-action models give visual navigation agents a way to anticipate how candidate actions will change future observations and to act from the predicted consequences. For drones, this capability must operate under tight accuracy and efficiency constraints. We present DroneWAM, an efficient world-action model for drone visual navigation. DroneWAM adopts a JEPA-based architecture to model future states directly in representation space, avoiding the cost of explicit future image generation. A pretrained Resampler further compresses dense encoder features into fewer latent tokens, reducing the computation repeated at each imagined step. We also introduce adaptive rollout, where a preference-trained Gate adaptively allocates prediction depth according to the current scene. To support learning under richer aerial motion, we construct DroneNav-6D, a simulated visual navigation dataset with synchronized RGB observations, 6-DoF flight trajectories, control commands, and randomized wind disturbances. On DroneNav-6D, DroneWAM achieves the best trajectory accuracy among the compared methods. Adaptive rollout further reduces the average prediction depth from 8 to 4.58 while improving trajectory accuracy, demonstrating that predictive computation can be allocated more effectively across scenes. \href{https://github.com/1e12Leon/DroneWAM}{Codes and data} will be released.